We continued to develop, refine and evaluate the National Cancer Institutes Breast Cancer Risk Assessment Tool (BCRAT). Using data from the Asian American Breast Cancer Study, we obtained ethnicity-specific relative risks and attributable risks for Asian American women, and we coupled these with age- and ethnicity-specific breast cancer incidence rates from SEER to produce absolute risks. We are in the final stages of evaluating this model before publication and incorporation into BCRAT. With a summer Fellow , Mateo Banegas, who is a pre-doctoral student at the University of Washington in Seattle, we quantified the performance of the BCRAT for Latina women in the Women's Health Initiative. We found that BCRAT underestimated risk somewhat, but that recalibration to more recent SEER rates improved the predictions of BCRAT. BCRAT (also called Gail model 2) uses age-specific breast cancer incidence rates and competing mortality rates from SEER from 1983-1987 to predict the absolute risk of invasive breast cancer. Motivated by changes in breast cancer incidence during the 1990s, we evaluated the model's calibration in Caucasian, postmenopausal women from the NIH-AARP cohort (1995-2003), and the PLCO Screening Trial (1993-2006). We assessed calibration by comparing the number of breast cancers expected (E) from the Gail model with that observed (O). We then evaluated the calibration using an updated model that combined Gail model relative risks with SEER invasive breast cancer incidence rates from the period corresponding to our cohorts, 1995-2003. Overall, the Gail model significantly underpredicted the number of invasive breast cancers by 13% in NIH-AARP, E/O=0.87 (95% CI: 0.85-0.89) and by 14% in PLCO, E/O=0.86 (95% CI: 0.82-0.90). The updated model was well-calibrated overall: E/O=1.03 (95% CI: 1.00-1.05) in NIH-AARP and E/O=1.01 (95% CI: 0.97-1.06) in PLCO. Subset analyses in PLCO suggested that the Gail model was well-calibrated in PLCO when prediction period was restricted to between 2003-2006. There is interest in determining whether SNP genotypes can add important predictive value to models such as BCRAT. Using data collected from 5590 case subjects and 5998 control subjects between 50 to 79 years of age from four U.S. cohort studies and one case-control study from Poland, we fit models of absolute risk based on information about risk factors used in BCRAT and 10 common genetic variants associated with breast cancer. We concluded that the inclusion of newly discovered genetic factors modestly increased the area under the receiver-operating characteristic curve, but the level of predicted breast-cancer risk among most women changed little after the addition of these SNP genotypes. Another study showed that adding ten SNPs to BCRAT produced only minimal improvements in the following applications: deciding whether to take tamoxifen to prevent breast cancer;deciding whether to have a mammogram;and allocating mammograms when there is not enough money to screen all women. Related work showed that it is unlikely that foreseeable genome-wide association studies will discover enough SNPs with sufficiently strong associations to make substantial further improvements in risk prediction. We analyzed data from the Washington Ashkenazi Study to address whether a woman from a high risk family known to carry mutations in BRCA1 or BRCA2 genes had above average risk of breast cancer even if she was found not to carry a mutation. Because most of the familial correlation in breast cancer risk is not due to BRCA1 or BRCA2 mutations, and because most high risk families are ascertained because several members are affected, there is reason to believe that such a woman remains at higher risk than the general population, even though the risk is not as high as for a mutation carrier. Our data and review of the literature indicate that such residual familial risk can affect clinical management. BRCAPRO is a model to predict who in a family carries a mutation in BRCA1 or BRCA2, based on family history of breast and ovarian cancer. We are extending BRCAPRO to account for potentially misreported family history. We developed separate absolute risk prediction models for breast cancer, ovarian cancer, and endometrial cancer using data from the NCI-AARP and the Prostate, Lung, Colorectal and Ovarian Cancer (PLCO) Screening Trial cohorts. We validated the models in the Nurses Health Study, in collaboration with investigators at Harvard University. A manuscript is in preparation. We are constructing a model based on HPV testing to help guide diagnostic testing and treatment of women at risk of cervical cancer. We estimated risk from 330,000 women screened in Kaiser-Permanente Northern California. We are developing a statistical model that will separate the screening protocol from natural history. This will allow us to estimate risk for any screening protocol. We proposed two criteria to assess the usefulness of models that predict risk of disease incidence for screening and prevention, or the usefulness of prognostic models for management following disease diagnosis. The first criterion, the proportion of cases followed PCF(q), is the proportion of individuals who will develop disease who are included in the proportion q of individuals in the population at highest risk. The second criterion is the proportion needed to follow-up, PNF(p), namely the proportion of the general population at highest risk that one needs to follow in order that a proportion p of those destined to become cases will be followed. PCF(q) assesses the effectiveness of a program that follows 100q% of the population at highest risk. PNF(p) assess the feasibility of covering 100p% of cases by indicating how much of the population at highest risk must be followed. We showed the relationship of those two criteria to the Lorenz curve and its inverse, and present distribution theory for estimates of PCF and PNF. We developed new methods, based on influence functions, for inference for a single risk model, and also for comparing the PCFs and PNFs of two risk models, both of which were evaluated in the same validation data. We developed imputation methods for projecting absolute risk of dying from an incident cancer in SEER when cause of death information is missing in some cases.